
'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_nllb import NllbTokenizer
else:
		UpperCAmelCase__ :			Optional[int]											=		None


UpperCAmelCase__ :			int											=		logging.get_logger(__name__)


UpperCAmelCase__ :			List[str]											=		{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__ :			List[Any]											=		{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__ :			List[str]											=		{
    "facebook/nllb-large-en-ro": 10_24,
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
UpperCAmelCase__ :			Union[str, Any]											=		["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Tuple   =  VOCAB_FILES_NAMES
	snake_case__   :Tuple   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :List[Any]   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Tuple   =  ['input_ids', 'attention_mask']
	snake_case__   :str   =  NllbTokenizer

	snake_case__   :List[int]   =  []
	snake_case__   :List[int]   =  []





	def __init__(       self      :							str   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							List[str]=None   ,				__magic_name__      :							List[Any]="<s>"   ,				__magic_name__      :							Any="</s>"   ,				__magic_name__      :							Tuple="</s>"   ,				__magic_name__      :							str="<s>"   ,				__magic_name__      :							Optional[int]="<unk>"   ,				__magic_name__      :							str="<pad>"   ,				__magic_name__      :							int="<mask>"   ,				__magic_name__      :							List[Any]=None   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Any=None   ,				__magic_name__      :							Optional[Any]=False   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				AddedToken(__magic_name__   ,				lstrip=__magic_name__   ,				rstrip=__magic_name__				) if isinstance(__magic_name__   ,				__magic_name__				) else mask_token
				lowerCAmelCase__										=				legacy_behaviour
				super().__init__(
				    vocab_file=__magic_name__   ,				tokenizer_file=__magic_name__   ,				bos_token=__magic_name__   ,				eos_token=__magic_name__   ,				sep_token=__magic_name__   ,				cls_token=__magic_name__   ,				unk_token=__magic_name__   ,				pad_token=__magic_name__   ,				mask_token=__magic_name__   ,				src_lang=__magic_name__   ,				tgt_lang=__magic_name__   ,				additional_special_tokens=__magic_name__   ,				legacy_behaviour=__magic_name__   ,				**__magic_name__   ,				)

				lowerCAmelCase__										=				vocab_file
				lowerCAmelCase__										=				False if not self.vocab_file else True

				lowerCAmelCase__										=				FAIRSEQ_LANGUAGE_CODES.copy()

				if additional_special_tokens is not None:
							# Only add those special tokens if they are not already there.
							_additional_special_tokens.extend(
							    [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

				self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}				)
				lowerCAmelCase__										=				{
				    lang_code: self.convert_tokens_to_ids(__magic_name__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				lowerCAmelCase__										=				src_lang if src_lang is not None else "eng_Latn"
				lowerCAmelCase__										=				self.convert_tokens_to_ids(self._src_lang				)
				lowerCAmelCase__										=				tgt_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

				return self._src_lang





	@src_lang.setter
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				new_src_lang
				self.set_src_lang_special_tokens(self._src_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							List[int]   ,				__magic_name__      :							Optional[List[int]] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				[self.sep_token_id]
				lowerCAmelCase__										=				[self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep				) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str]   ,				__magic_name__      :							Optional[str]   ,				**__magic_name__      :							int				):


				"""simple docstring"""

				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				self(__magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)
				lowerCAmelCase__										=				self.convert_tokens_to_ids(__magic_name__				)
				lowerCAmelCase__										=				tgt_lang_id
				return inputs





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							str = "eng_Latn"   ,				__magic_name__      :							Optional[List[str]] = None   ,				__magic_name__      :							str = "fra_Latn"   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				src_lang
				lowerCAmelCase__										=				tgt_lang
				return super().prepare_seqaseq_batch(__magic_name__   ,				__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				return self.set_src_lang_special_tokens(self.src_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self.set_tgt_lang_special_tokens(self.tgt_lang				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							List[str]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.convert_tokens_to_ids(__magic_name__				)

				if self.legacy_behaviour:
							lowerCAmelCase__										=				[]
							lowerCAmelCase__										=				[self.eos_token_id, self.cur_lang_code]
				else:
							lowerCAmelCase__										=				[self.cur_lang_code]
							lowerCAmelCase__										=				[self.eos_token_id]

				lowerCAmelCase__										=				self.convert_ids_to_tokens(self.prefix_tokens				)
				lowerCAmelCase__										=				self.convert_ids_to_tokens(self.suffix_tokens				)

				lowerCAmelCase__										=				processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,				pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,				self.prefix_tokens + self.suffix_tokens				)				)   ,				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.convert_tokens_to_ids(__magic_name__				)
				if self.legacy_behaviour:
							lowerCAmelCase__										=				[]
							lowerCAmelCase__										=				[self.eos_token_id, self.cur_lang_code]
				else:
							lowerCAmelCase__										=				[self.cur_lang_code]
							lowerCAmelCase__										=				[self.eos_token_id]

				lowerCAmelCase__										=				self.convert_ids_to_tokens(self.prefix_tokens				)
				lowerCAmelCase__										=				self.convert_ids_to_tokens(self.suffix_tokens				)

				lowerCAmelCase__										=				processors.TemplateProcessing(
				    single=prefix_tokens_str + ["$A"] + suffix_tokens_str   ,				pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,				self.prefix_tokens + self.suffix_tokens				)				)   ,				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."				)

				if not os.path.isdir(__magic_name__				):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""				)
							return
				lowerCAmelCase__										=				os.path.join(
				    __magic_name__   ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

				if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				):
							copyfile(self.vocab_file   ,				__magic_name__				)

				return (out_vocab_file,)













"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records